GPT-4.1 Nano vs GPT-5.4 Nano
At a Glance
| Compare |  GPT-4.1 NanoOpenAI | GPT-5.4 NanoOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | UnrankedNot in the 57-model eligible cohort | #38 of 5715.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #11 of 27$0.058 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #26 of 2729.9 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.050Openrouter ↗ · Sep 3, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.20Openrouter ↗ · Sep 3, 2026 | $1.25Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,048K | 400K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | GPT-4.1 Nano | GPT-5.4 Nano |
|---|---|---|
| LMArena Text Arenatext-2026-10-15-0ebb411abf00 · arena_rating · leader | 1,284.7894% of row best · rating · gpt-4.1-nano-2025-04-14; 95% CI [1277.13551573, 1292.41837024]; votes 6103; rank 264 | 1,372.01100% of row best · rating · gpt-5.4-nano-high; 95% CI [1368.25704083, 1375.76986498]; votes 62396; rank 182 |
| LMArena Vision Arenavision-2026-10-09-0ebb411abf00 · arena_rating · leader | 1,063.2489% of row best · rating · gpt-4.1-nano-2025-04-14; 95% CI [1044.95158579, 1081.52393740]; votes 1211; rank 137 | 1,195.96100% of row best · rating · gpt-5.4-nano-high; 95% CI [1189.64874058, 1202.27965668]; votes 25650; rank 94 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | GPT-4.1 Nano | GPT-5.4 Nano |
|---|---|---|
| Developer | OpenAI | OpenAI |
| Family | Gpt 4 1 | Gpt 5 4 |
| Model | GPT-4.1 Nano | GPT-5.4 Nano |
| Version | GPT-4.1 Nano | GPT-5.4 Nano |
| Lifecycle | active | active |
| Released | Unknown | 2026-03-17 |
| Knowledge cutoff | 2024-06-01 | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,048K | 400K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Openai (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, structured_outputs, tools |
GPT-4.1 Nano Capabilities
GPT-5.4 Nano Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
 MiniMax-M3MiniMax | vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text |
 Nova 2 LiteAmazon | vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text |
Claude Mythos 5.1Anthropic | vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text |
 MolmoWeb 8BAi2 | vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text |
Claude Opus 5Anthropic | vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text |
 Gemini Deep ResearchGoogle DeepMind | vs | GPT-4.1 NanoOpenAI | cross-developer peerstext |
Gemini Computer UseGoogle DeepMind | vs | GPT-4.1 NanoOpenAI | cross-developer peerstext |
Gemini 3.5 Flash-LiteGoogle DeepMind | vs | GPT-4.1 NanoOpenAI | cross-developer peerstext |
| vs | GPT-4.1 NanoOpenAI | cross-developer peersimage, text | |
GPT-4.1 NanoOpenAI | vs | GPT-5 NanoOpenAI | family variantsimage, text |
GPT-4.1 NanoOpenAI | vs | GPT-5.6 LunaOpenAI | family variantsimage, text |
Primary Evidence
Sources and Freshness
Questions
GPT-4.1 Nano vs GPT-5.4 Nano FAQs
Is GPT-4.1 Nano or GPT-5.4 Nano better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4.1 Nano and GPT-5.4 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, GPT-4.1 Nano or GPT-5.4 Nano?+
GPT-4.1 Nano is $0.050 and GPT-5.4 Nano is $0.20 per million tokens, so GPT-4.1 Nano is cheaper on this metric. GPT-4.1 Nano is $0.20 and GPT-5.4 Nano is $1.25 per million tokens, so GPT-4.1 Nano is cheaper on this metric.
Which has a larger context window, GPT-4.1 Nano or GPT-5.4 Nano?+
GPT-4.1 Nano has the larger sourced context window. GPT-4.1 Nano supports 1,047,576 tokens and GPT-5.4 Nano supports 400,000 tokens.
Which performs better in benchmarks, GPT-4.1 Nano or GPT-5.4 Nano?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can GPT-4.1 Nano or GPT-5.4 Nano be self-hosted?+
Both models have the same sourced self-hosting status: unsupported. GPT-4.1 Nano is not marked open weight; GPT-5.4 Nano is not marked open weight.
Can GPT-4.1 Nano and GPT-5.4 Nano understand images?+
GPT-4.1 Nano is documented with image input; GPT-5.4 Nano is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, GPT-4.1 Nano or GPT-5.4 Nano?+
GPT-5.4 Nano has the larger sourced maximum output. GPT-4.1 Nano supports 32,768 tokens and GPT-5.4 Nano supports 128,000 tokens.
Do GPT-4.1 Nano and GPT-5.4 Nano support reasoning and tool use?+
GPT-4.1 Nano: tool calling and image input. GPT-5.4 Nano: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, GPT-4.1 Nano or GPT-5.4 Nano?+
GPT-4.1 Nano has 2 sourced provider routes; GPT-5.4 Nano has 2, a tie.
Which offers better value, GPT-4.1 Nano or GPT-5.4 Nano?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.